NDLI: Feedback Particle Filter

Content Provider	IEEE Xplore Digital Library
Author	Tao Yang Mehta, P.G. Meyn, S.P.
Copyright Year	1963
Abstract	The feedback particle filter introduced in this paper is a new approach to approximate nonlinear filtering, motivated by techniques from mean-field game theory. The filter is defined by an ensemble of controlled stochastic systems (the particles). Each particle evolves under feedback control based on its own state, and features of the empirical distribution of the ensemble. The feedback control law is obtained as the solution to an optimal control problem, in which the optimization criterion is the Kullback-Leibler divergence between the actual posterior, and the common posterior of any particle. The following conclusions are obtained for diffusions with continuous observations: 1) The optimal control solution is exact: The two posteriors match exactly, provided they are initialized with identical priors. 2) The optimal filter admits an innovation error-based gain feedback structure. 3) The optimal feedback gain is obtained via a solution of an Euler-Lagrange boundary value problem; the feedback gain equals the Kalman gain in the linear Gaussian case. Numerical algorithms are introduced and implemented in two general examples, and a neuroscience application involving coupled oscillators. In some cases it is found that the filter exhibits significantly lower variance when compared to the bootstrap particle filter.
Sponsorship	IEEE Control Systems Society
Starting Page	2465
Ending Page	2480
Page Count	16
File Size	4216745
File Format	PDF
ISSN	00189286
Volume Number	58
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Technological innovation Optimal control Equations Kalman filters Approximation methods Particle filters particle filtering Mean-field games nonlinear filtering optimal transportation
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Feedback particle filter with mean-field coupling

The continuous-discrete time feedback particle filter

A mean-field control-oriented approach to particle filtering

Nonlinear filtering algorithms in object tracking applications

Computationally Efficient Kalman Filtering for a Class of Nonlinear Systems

Optimal estimation for continuous-time systems with delayed measurements

Filtering Quantized Measurements

A discontinuous mean-square filter for stochastic differential systems

Learning in Mean-Field Games

Feedback Particle Filter

Similar Documents

Feedback particle filter with mean-field coupling

The continuous-discrete time feedback particle filter

A mean-field control-oriented approach to particle filtering

Nonlinear filtering algorithms in object tracking applications

Computationally Efficient Kalman Filtering for a Class of Nonlinear Systems

Optimal estimation for continuous-time systems with delayed measurements

Filtering Quantized Measurements

A discontinuous mean-square filter for stochastic differential systems

Learning in Mean-Field Games

Feedback Particle Filter